Trends 2021: Machine Learning

Stephen DeAngelis

February 4, 2021

In both the academic and business arenas, there is a lot of buzz about artificial intelligence (AI); however, many computer experts point out that the term applies to so many different techniques it is often meaningless. Eric Siegel (@predictanalytic), a former computer science professor at Columbia University, writes, “The much better, precise term would instead usually be machine learning — which is genuinely powerful and everyone oughta be excited about it.”[1] Why is machine learning (ML) such a big deal? Technology writer Priya Dialani (@priyadialani248) asserts, “77% of devices that we presently use are utilizing ML.” Although purists will continue to object, AI and ML will continue to be used synonymously well into the future. Journalist Cindy Baker writes, “Experts predict artificial intelligence and machine learning will enter a golden age in 2021, solving some of the hardest business problems.”[3] Below are some of the machine learning trends subject matter experts predict will have an impact in the coming year.

 

Trends in machine learning

 

Machine learning and the Internet of Things (IoT). Dialani notes, “The Internet of Things has been a quickly developing segment recently with economic analyst Transforma Insights forecasting that the worldwide IoT market will develop to 24.1 billion devices in 2030, producing $1.5 trillion in income. The utilization of machine learning is progressively interlaced with IoT. Machine learning, artificial intelligence, deep learning, for instance, are now being utilized to make IoT devices and services smarter and more secure.” ML and IoT are natural companions since machine learning requires a lot of data and the IoT can help generate data. Rick Whiting (@RickWhiting1), a senior editor with The Channel Company, notes, “What some are calling ‘Artificial Intelligence of Things’ (AIoT) could redefine industrial automation.”[4] Baker adds, “The combination [of ML and IoT] is so powerful that Gartner predicts that by 2022, more than 80 per cent of enterprise IoT projects will incorporate AI in some form, up from just 10 per cent today.” Analysts from Simplilearn rhetorically ask, “Why do these two technologies work so well together?” Their answer, “You can think of IoT as the digital nervous system and AI as the brain that makes the decisions. AI’s ability to rapidly glean insights from data makes IoT systems more intelligent.”[5]

 

Machine Learning at the Edge. For a number of reasons, including limited bandwidth, momentum is building for doing more processing “at the edge” instead of transmitting all data back to a central processor. Martin Isaksson (@martinisaksson_) Co-Founder and CEO of PerceptiLabs, explains, “We’re seeing a growing trend towards inference at the edge, a segment we expect will grow substantially in 2021. There are a number of factors for this including the growth of IoT and greater reliance on devices for remote work.”[5] Closely associated with edge computing is TinyML. Tech writers Evan Schuman and Lauren Horwitz (@lhorwitz) explain, “In recent years, hardware advancements have enabled the microcontrollers that perform calculations much faster. Improved hardware combined with more efficient development practices have made it easier for developers to build programs on these devices. … Tiny machine learning, or TinyML, has ushered in this era of smarter hardware. TinyML encompasses technologies capable of performing on-device analytics garnered from sensor data at low power. Together with hardware advancement and progress in ML development, microcontrollers can now run increasingly complex ML models directly on the hardware and without a round trip to the cloud.”

 

Machine Learning and Hyperautomation. Dialani notes, “Hyperautomation, an IT mega-trend identified by Gartner, is the possibility that almost anything inside a company that can be automated — for example, legacy business processes — should be automated. The pandemic has boosted the adoption of the concept, which is otherwise called ‘digital process automation’ and ‘intelligent process automation.’ Machine learning and artificial intelligence are key segments and significant drivers of hyperautomation.” Whiting adds, “To be successful hyperautomation initiatives cannot rely on static packaged software. Automated business processes must be able to adapt to changing circumstances and respond to unexpected situations. That’s where AI, machine learning models and deep learning technology come in, using ‘learning’ algorithms and models, along with data generated by the automated system, to allow the system to automatically improve over time and respond to changing business processes and requirements.”

 

Machine Learning and Augmented Intelligence. Simplilearn analysts write, “For those [who] may still be worried about AI cannibalizing their jobs, the rise of Augmented Intelligence should be a refreshing trend. It brings together the best capabilities of both humans and technology, giving organizations the ability to improve the efficiency and performance of their workforce. By 2023, Gartner predicts that 40% of infrastructure and operations teams in large enterprises will use AI-augmented automation, resulting in higher productivity. Naturally, their employees should be skilled in data science and analytics or get the opportunity to upskill on the latest AI and ML technologies to achieve optimal results.” Generally, augmented intelligence is associated with cognitive computing (which includes machine learning in its suite of capabilities).

 

Machine Learning and Cybersecurity. According to Yossi Sheffi (@YossiSheffi), the Elisha Gray II Professor of Engineering Systems at MIT, data is now a company’s most important asset. He writes, “The well-worn adage that a company’s most valuable asset is its people needs an update. Today, it’s not people but data that tops the asset value list for companies.”[8] It only makes sense that companies want to protect that asset. In addition, companies need to protect the personal data they store on individuals. Failing to do so can cause them irreparable harm. Simplilearn analysts note, “Data is the new currency. In other words, it’s the most valuable resource that organizations need to protect. With AI and ML thrown into the mix, it’s only going to increase the amount of data they handle and the risks associated with it. … Regulations like GDPR and, more recently, the California Consumer Privacy Act — which [became] effective in 2020 — have made privacy violations very expensive.” Fortunately, writes Whiting, “Artificial intelligence and machine learning technology is increasingly finding its way into cybersecurity systems for both corporate systems and home security. Developers of cybersecurity systems are in a never-ending race to update their technology to keep pace with constantly evolving threats from malware, ransomware, DDS attacks and more. AI and machine learning technology can be employed to help identify threats, including variants of earlier threats.”

 

Machine Learning and Ethics. As machine learning is implemented in more and more ways, concerns about its ethical use are also rising. Whiting notes, “[The concern about racial injustice] has put the spotlight on a range of ethical questions around the increasing use of artificial intelligence technology. That includes the obvious misuse of AI for ‘deepfake’ misinformation efforts and for cyberattacks. But it also includes grayer areas such as the use of AI by governments and law enforcement organizations for surveillance and related activities and the use of AI by businesses for marketing and customer relationship applications.” Jin-Whan Jung, Senior Director & Leader of the Advanced Analytics Lab at SAS, believes “responsible AI will become a high priority for executives in 2021.”[9] He adds, “There is a possibility for bias in the machine, the data or the way we train the model. We have to make every effort to have processes and gatekeepers to double and triple check to ensure compliance, privacy and fairness.” Baker adds, “Gartner also recommends the creation of an external AI ethics board to advise on the potential impact of AI projects.”

 

Concluding thoughts

 

The use of machine learning is only going to grow and devices embedded with machine learning will continue to proliferate into almost every economic sector. As more and more companies transform into digital enterprises, they will be forced to hire machine learning experts or hire vendors who can provide them with that expertise.

 

Footnotes
[1] Eric Siegel, “Why A.I. is a big fat lie,” Big Think, 23 January 2019.
[2] Priya Dialani, “Top 6 Machine Learning Trends of 2021,” Analytics Insight, 25 November 2020.
[3] Cindy Baker, “Five real world AI and machine learning trends that will make an impact in 2021,” IT World Canada, 6 January 2021.
[4] Rick Whiting, “5 Emerging AI And Machine Learning Trends To Watch In 2021,” CRN, 23 Oct 2020.
[5] Staff, “5 Emerging Machine Learning and AI Trends To Watch in 2021,” Simplilearn, 4 December 2020.
[6] Martin Isaksson, “What’s next? Machine Learning 2021,” Towards Data Science, 17 December 2020.
[7] Evan Schuman and Lauren Horwitz, “Predictions for Embedded Machine Learning for IoT in 2021,” IoT World Today, 10 December 2020.
[8] Yossi Sheffi, “What is a Company’s Most Valuable Asset? Not People,” Supply Chain @ MIT, 20 December 2018.
[9] Baker, op. cit.